Building AI Features Users Actually Want

●TL;DR

80% of AI features fail to reach adoption thresholds. The survivors solve specific, measurable user pain points... not "we should add AI." User research must validate that AI is the right solution before engineering begins. Build vs buy: APIs for commodity capabilities, fine-tuning for differentiation, custom models only when you ARE the AI. Gradual rollout with feature flags and explicit feedback loops. Measure outcomes, not engagement.

Part of the AI-Assisted Development Guide ... from code generation to production LLMs.

●The AI Feature Graveyard

Every SaaS product manager has a backlog item that reads "Add AI to [feature]." This is how AI features die.

I have conducted post-mortems on 12 AI feature launches in the past two years. Eight of them were rolled back or deprecated within six months. The surviving four shared one characteristic: they began with a specific user problem, not with AI as a starting point.

The graveyard is full of good intentions:

• AI-powered search that returned worse results than keyword matching
• Smart recommendations that users learned to ignore after three irrelevant suggestions
• Auto-complete that completed to wrong answers faster than users could type the right ones
• AI summarization that missed the detail users actually needed

These failures cost engineering time, increased technical debt, and... worst of all... trained users to distrust AI features when a genuinely useful one eventually shipped.

The pattern is consistent: teams start with "we should add AI" and work backward to find a use case. The successful features start with a user problem and evaluate whether AI is the right tool to solve it.

●User Research for AI Features

Standard product research asks "what do users want?" AI feature research must ask a harder question: "where does AI add value versus annoyance?"

The AI Appropriateness Filter

Not every problem benefits from AI. Before writing a single prompt, validate that AI is appropriate for your use case:

AI excels at:

• Pattern recognition across large datasets
• Summarizing or transforming content
• Generating starting points that humans refine
• Automating repetitive cognitive tasks
• Handling ambiguous inputs with reasonable defaults

AI struggles with:

• Precision-critical calculations (use deterministic code)
• Legal or compliance determinations (liability issues)
• Tasks requiring perfect accuracy (one error destroys trust)
• Decisions users want to make themselves (autonomy matters)
• Simple operations that feel over-engineered with AI

A SaaS I advised wanted to use AI to calculate invoice totals. The reasoning: "AI can handle edge cases like discounts and tax." The reality: deterministic code handles these edge cases perfectly, and a single wrong invoice amount would have destroyed customer trust. AI was the wrong tool.

The User Interview Framework

When researching AI features, standard interview techniques miss critical signals. Modify your approach:

Instead of: "Would you like AI to help with [task]?"

Users almost always say yes. It is the novelty effect combined with politeness. This question tells you nothing.

Ask instead: "Walk me through the last time you did [task]. What was the hardest part?"

Listen for:

• Time-consuming repetitive steps (automation candidates)
• Decisions made with incomplete information (augmentation candidates)
• Tasks they delegate but wish they could verify (analysis candidates)
• Manual data processing before the actual work (transformation candidates)

Dig deeper: "If we could reduce that from [current time] to [faster time], what would you do differently?"

This reveals whether the pain point actually matters. Users complain about many inconveniences they would not pay money or change behavior to solve.

The Job-to-Be-Done Mapping

Map user jobs to AI capability categories:

The validation questions are critical. They surface whether users will trust AI output and whether errors are recoverable.

Observational Research

Interviews capture stated preferences. Observation captures actual behavior.

Shadow 5-10 users performing the task you want to augment with AI. Document:

• Actual time spent (users underestimate routine tasks)
• Context switching (AI works best for focused, repetitive work)
• Error recovery patterns (how do they catch and fix mistakes?)
• Output variation (high variation suggests human judgment is needed; low variation suggests automation)

One product team I worked with planned an AI feature to help users write marketing copy. Observation revealed that users spent 30% of their time writing and 70% finding and organizing source material. The AI feature they built... smart content organization... succeeded. The AI copywriting feature they almost built would have failed.

●The AI Feature Framework

After validating that AI is appropriate for your use case, categorize the feature by its role in the user workflow.

Automation: AI Does the Work

The user is removed from the loop entirely. AI performs a task end-to-end without human intervention.

Examples:

• Auto-tagging incoming support tickets
• Spam filtering
• Image optimization during upload
• Duplicate detection and merging

Requirements for Automation:

• Error tolerance is high (wrong tags are corrected; wrong spam filtering is visible)
• The task is clearly defined with limited scope
• Users can verify outputs without re-doing the work
• Failures fail gracefully (flag for human review rather than corrupting data)

User Experience Pattern: The best automation is invisible. Users should not know AI is involved unless something goes wrong. Gmail does not announce "AI filtered 47 spam messages today." The absence of spam is the experience.

Augmentation: AI Assists the Work

The user remains in control. AI provides suggestions, drafts, or analysis that the user reviews and modifies.

Examples:

• Writing suggestions and autocomplete
• Recommended next actions
• Draft responses to customer inquiries
• Highlighted anomalies in data

Requirements for Augmentation:

• Clear visual distinction between AI suggestions and user content
• Easy acceptance and rejection of suggestions
• Graceful degradation if AI is slow or unavailable
• Training data improves from user corrections

User Experience Pattern: Augmentation must be interruptible. Users should be able to ignore suggestions without friction. GitHub Copilot succeeds because Tab accepts and any other key dismisses. The interaction cost of rejection is near zero.

Analysis: AI Surfaces Insights

AI processes data to identify patterns, trends, or risks that users would not find manually.

Examples:

• Churn prediction scores
• Revenue forecasting
• Content performance analysis
• Security threat detection

Requirements for Analysis:

• Explainability... users need to understand why the AI reached its conclusion
• Confidence indicators... not all predictions are equal
• Action pathways... what should the user do with this information?
• Feedback loops... was the prediction accurate?

User Experience Pattern: Analysis features must connect to action. A dashboard that shows "15 customers at high churn risk" is useless without "here is what typically works to retain them." Insights without action paths become ignored.

●Build vs Buy for AI

The build vs buy decision is even more pronounced for AI. The technical complexity is higher, the talent is scarcer, and the opportunity cost is larger.

When to Use APIs

Use third-party APIs (OpenAI, Anthropic, Google, etc.) when:

• The capability is commoditized (summarization, translation, general Q&A)
• You lack ML engineers on staff
• Time-to-market is critical
• Your use case does not require domain-specific knowledge
• API costs at your scale are acceptable

Example calculation:

A B2B SaaS with 5,000 MAU wants to add AI-powered document summarization. Each user summarizes 10 documents per month. Average document is 5,000 tokens. Output is 500 tokens.

Monthly API volume: 5,000 × 10 × 5,500 = 275 million tokens

At $0.002 per 1K tokens (input) and $0.006 per 1K tokens (output):

• Input cost: 250M × $0.002 = $500
• Output cost: 25M × $0.006 = $150
• Total: $650/month

Hiring one ML engineer to build custom: $200K/year + infrastructure + 6 months to production.

The API is correct until you hit roughly 50K MAU with this usage pattern.

When to Fine-Tune

Fine-tuning a base model makes sense when:

• Your domain has specialized vocabulary or patterns
• General models produce adequate but not excellent results
• You have labeled data from your actual use case
• The improvement is measurable and meaningful to users

Fine-tuning economics:

Fine-tuning a model costs $100-1,000 for the training run (depending on data size and model). The ongoing cost reduction comes from using a smaller, faster model that performs as well as a larger one for your specific use case.

A legal tech company I advised fine-tuned Llama 3 8B on their contract review dataset. The fine-tuned model matched GPT-4 quality for their specific task at 1/20th the inference cost.

Warning: Fine-tuning requires labeled data. If you do not have 1,000+ examples of correct outputs for your use case, fine-tuning will not help.

When to Build Custom Models

Build custom models when:

• You ARE an AI company (this is your core product)
• Your use case cannot be addressed by any existing model
• You have unique training data that creates competitive advantage
• The model is your moat, not a feature

The reality check:

Fewer than 5% of companies adding AI features should build custom models. If you are not already employing ML researchers, you are not in this category.

The cost to train a competitive model from scratch starts at $1M and scales to hundreds of millions for frontier models. The talent market for ML researchers is among the most competitive in technology.

For everyone else, the future is fine-tuning base models, not training from scratch.

●Gradual Rollout Strategy

AI features are probabilistic. They will behave differently than users expect at least some of the time. Gradual rollout reduces blast radius and generates learning.

Feature Flag Architecture

Every AI feature should be behind a feature flag with multiple levels:

interface AIFeatureConfig {
	enabled: boolean;
	rolloutPercentage: number; // 0-100
	userSegments: string[]; // 'beta', 'enterprise', 'free', etc.
	fallbackBehavior: "hide" | "manual" | "cached";
	confidenceThreshold: number; // 0-1, hide results below this
}

const aiSummarization: AIFeatureConfig = {
	enabled: true,
	rolloutPercentage: 10,
	userSegments: ["beta", "enterprise"],
	fallbackBehavior: "manual",
	confidenceThreshold: 0.85,
};

The Rollout Sequence

Phase 1: Internal Dogfooding (Week 1-2)

Ship to internal users only. Instrument heavily. Collect explicit feedback.

Watch for:

• Latency issues (AI operations can be slow)
• Error rates (API failures, timeout, malformed responses)
• Quality complaints (wrong or unhelpful output)
• Edge cases (inputs the model handles poorly)

Do not proceed until internal users are satisfied.

Phase 2: Opt-In Beta (Week 3-4)

Invite power users to opt in. These users are forgiving and provide detailed feedback.

Provide a clear feedback mechanism:

• Thumbs up/down on AI outputs
• "This was wrong" with optional explanation
• "This was exactly what I needed"

Track feedback signals aggressively. A thumbs-down ratio above 15% indicates the feature is not ready.

Phase 3: Segment Rollout (Week 5-8)

Roll out by user segment, not random percentage. Segments provide cleaner signal:

• Enterprise users (high expectations, detailed feedback)
• New users (no baseline to compare against)
• Power users (push the feature to edge cases)
• Free tier (lower stakes, higher volume)

Compare metrics between segments. If enterprise users love it but free users ignore it, you have a pricing/positioning signal.

Phase 4: General Availability (Week 9+)

Roll out to remaining users. Monitor for:

• Support ticket volume related to the feature
• Feature discovery (are users finding it?)
• Retention impact (are users who use the feature more engaged?)
• Revenue impact (are users upgrading for this feature?)

●Measuring AI Feature Success

Engagement metrics lie. A user who clicks on AI-generated suggestions twenty times and ignores all of them is not getting value.

Outcome Metrics

Measure what the AI feature is supposed to accomplish, not interactions with it.

The Accuracy-Utility Gap

Accuracy and utility are not the same.

An AI that is 95% accurate sounds good. But if users must verify every output because they cannot trust the 5% of errors, the utility is zero. They do the full work anyway.

An AI that is 80% accurate but clearly communicates confidence can be more useful. Users know when to trust it and when to verify.

Measure perceived accuracy, not just actual accuracy:

Survey users: "How often does [AI feature] give you the right answer on the first try?"

If perceived accuracy is lower than actual accuracy, you have a trust problem. If it is higher, you have a dangerous overconfidence problem.

Retention Cohort Analysis

Compare retention between users who adopt the AI feature and users who do not.

Caution: Self-selection bias is real. Users who try new features are often more engaged baseline. Control for:

• Days since signup (newer users try more features)
• Plan tier (paid users explore more)
• Historical engagement (some users try everything)

Use propensity score matching or diff-in-diff analysis to isolate the feature impact from user characteristics.

The Abandonment Signal

AI features have a unique failure mode: users try them, find them unhelpful, and never return.

Track:

• First-to-second use rate: What percentage of users who try the feature once try it again?
• Time between uses: Increasing gaps suggest declining utility
• Feature dormancy: Users who used the feature regularly and stopped

A feature with 80% first-time trial rate and 15% second-use rate is failing. Users are curious but unimpressed.

●Managing User Expectations

AI features generate expectations that traditional features do not. Users expect magic. They get probability.

Communicating Limitations

Every AI feature needs a calibration moment... an early experience that teaches users what to expect.

Patterns that work:

• "AI-generated draft. Please review before sending."
• "Based on similar patterns. May not apply to your specific case."
• "Confidence: High / Medium / Low" indicators
• "Not seeing what you expected? Provide feedback."

Patterns that fail:

• "AI-Powered!" badges that promise and under-deliver
• No indication that AI is involved (users are surprised by errors)
• "Beta" labels used indefinitely (users lose patience)
• Overconfident presentation ("The AI determined...")

Error Handling That Preserves Trust

When AI fails... and it will... the error experience determines whether users try again.

Graceful degradation patterns:

async function getAISummary(document: Document): Promise<SummaryResult> {
	try {
		const summary = await aiService.summarize(document);

		if (summary.confidence < 0.7) {
			return {
				type: "low-confidence",
				content: summary.text,
				message: "This summary may be incomplete. Review the original document.",
			};
		}

		return { type: "success", content: summary.text };
	} catch (error) {
		// AI service unavailable - fall back gracefully
		return {
			type: "unavailable",
			message: "AI summary temporarily unavailable. Try again in a few minutes.",
			fallbackAction: "show-manual-summary-option",
		};
	}
}

Never fail silently. Users who expect AI output and get nothing will blame your product, not the AI.

The Feedback Loop Contract

Users who provide feedback expect it to matter. If feedback does not influence the system, stop collecting it.

Useful feedback patterns:

• Thumbs up/down that trains a reranking model
• "Wrong" reports that flag for human review and model retraining
• Explicit corrections that become training data
• "Show me why" explanations that surface the AI's reasoning

If you cannot use feedback to improve the system, at minimum use it to filter low-quality outputs from that user's future experience.

●The Anti-Patterns

Recognizing bad AI product strategy is as important as recognizing good strategy.

AI Washing

Adding AI to a feature name without changing the underlying capability. "Smart Search" that is keyword search with a different label. "AI-Powered Analytics" that is the same charts with a new icon.

Users learn to distrust AI claims quickly. AI washing poisons the well for genuine AI features.

The test: If you removed the AI component, would the feature be meaningfully worse? If not, it is AI washing.

Solution in Search of a Problem

Building AI capabilities first, then finding uses for them. "We have access to GPT-4. What should we build?"

This inverts the product development process. The result is technically impressive features that solve no user problem.

The fix: AI-assisted development should accelerate solving identified problems, not generate new features.

The Autonomous Fantasy

Assuming AI can replace human judgment entirely. Fully autonomous agents that make decisions without user oversight.

For most applications, humans should remain in the loop. The job of AI is to reduce cognitive load, not eliminate cognition.

The reality: Even autonomous vehicles... the most-hyped autonomous AI application... require human oversight for edge cases. Your SaaS feature is not more autonomous than a self-driving car.

Ignoring Error Cost Asymmetry

Treating false positives and false negatives as equivalent. They rarely are.

For spam filtering: false positive (blocking a real email) is much worse than false negative (letting spam through).

For fraud detection: false negative (missing fraud) is much worse than false positive (flagging a legitimate transaction for review).

Tune your AI for the error type that matters, not overall accuracy.

Copying Without Context

Adding features because competitors have them. "Competitor X added AI summarization. We need AI summarization."

Competitors may be:

• AI washing (the feature is not actually AI)
• Failing (the feature is not working)
• Serving different users (their users want it; yours may not)
• A year ahead on data (they can fine-tune; you cannot)

Competitive analysis reveals what to investigate, not what to build.

●Conclusion

The AI feature graveyard is full of products that started with "add AI" and ended with abandoned code and confused users.

The survivors started with a user problem. They validated that AI was the appropriate solution... not just a possible solution. They rolled out gradually, measured outcomes instead of engagement, and built feedback loops that improved the system over time.

Before your next AI feature:

1. Research the problem first. Observe users. Map their jobs to AI capabilities. Validate that AI solves a real pain point.

2. Choose the right AI role. Automation, augmentation, or analysis. Each has different requirements and user experience patterns.

3. Make the build vs buy decision explicitly. APIs for commodity capabilities. Fine-tuning for domain differentiation. Custom models only if AI is your product.

4. Roll out gradually. Feature flags, user segments, feedback loops. Learn before scaling.

5. Measure outcomes. Time saved, errors reduced, decisions improved. Not clicks, not engagement, not "AI interactions."

6. Communicate honestly. Set expectations. Handle errors gracefully. Build trust through transparency.

AI features that solve real problems create lasting competitive advantage. AI features that exist because "we should add AI" become maintenance burden and user frustration.

The difference is not technical. It is product strategy.

Building AI features that actually solve user problems? I help teams navigate the product strategy, technical architecture, and user research required to ship AI features that stick.

• AI Integration for SaaS ... From strategy to production
• Next.js Development for SaaS ... Full-stack AI feature development
• Technical Advisor for Startups ... Strategic guidance on AI integration

●Continue Reading

This post is part of the AI-Assisted Development Guide ... covering code generation, LLM architecture, prompt engineering, and cost optimization.

More in This Series

• AI-Assisted Development: Navigating the Generative Debt Crisis ... The hidden costs of AI-generated code
• LLM Integration Architecture ... Vector databases to production
• Prompt Engineering for Developers ... Getting better LLM results
• AI Code Review ... Catching what LLMs miss
• AI Cost Optimization ... APIs vs self-hosting vs fine-tuning

Integrating AI into your product? Work with me on your AI architecture.